Abstract: The present invention generally describes a method for classifying a line segment of a handwritten line into a reference feature set, wherein said handwritten line comprises one or several curves representing a plurality of symbols. First, sample data representing said handwritten line is received. Next, a sample line segment in said received sample data is identified by detecting a sample line segment start point (SLSSP) and a sample line segment end point (SLSEP). Then, a sample feature set of said identified sample line segment is determined. Finally, the determined sample feature set is matched to a reference feature set among a plurality of reference feature sets.

Abstract: A method for recognition of a handwritten pattern comprising one or more curves is presented. The method comprises a step of receiving sample data representing the handwritten pattern. The method further comprises a step of segmenting the handwritten pattern by detecting segmentation points on each curve, and by dividing the handwritten pattern into segments. Further, the method comprises a step of comparing the handwritten pattern to templates wherein the comparing comprises a step of normalizing said segments according to a scheme which is independent of the templates to which the segments are to be compared, and a step of determining matching measures for selecting at least one sequence of templates representing a recognintion candidate of the handwritten pattern.

Abstract: A method for determining at least one recognition candidate for a handwritten pattern comprises selecting possible segmentation points in the handwritten pattern for use in segmenting and recognizing the handwritten pattern. The method further may comprise comparing segments of the handwritten pattern to templates. The comparison may return segment candidates forming possible recognition results of the segments of the handwritten pattern. The method further comprises forming a representation of sequences of segment candidates, said representation comprising data blocks corresponding to segmentation points, wherein a data block comprises references to data blocks corresponding to subsequent segmentation points. The reference may comprise information of segment candidates.

Abstract: A method for presenting recognized handwritten symbols includes recognizing a detected handwritten pattern. Recognizing may include comparing the handwritten pattern to templates representing ways of writing symbols and returning a best interpretation of the handwritten pattern. The best interpretation may be based on the pattern of a best template. At least two templates may include different patterns which represent different ways of writing a single symbol, and the patterns of the at least two templates may return different best interpretations when being most similar to the handwritten pattern. The method further includes presenting the pattern of the best template on a screen. A device for implementing the method may also be provided.

Abstract: A method for recognition of a handwritten pattern comprises selecting core points among a sequence of detected points of the handwritten pattern. The core points are selected for use in segmenting and recognizing the handwritten pattern. The method may further comprise identifying potential diacritics in the sequence of points of the handwritten pattern, determining features of each core point, assigning at least one feature associated with identified potential diacritics to each core point of a subset of core points, and comparing the handwritten pattern to templates.

Abstract: A method for recognition of a handwritten pattern comprises selecting core points among a sequence of detected points of the handwritten pattern. The core points are selected for use in segmenting and recognizing the handwritten pattern. The method further comprises determining features of each core point, and comparing the handwritten pattern to templates. The comparing comprises stepwise analyzing the core points in sequence by matching the features of sequences of core points that either start with the first core point or the last core point of a previous sequence to said templates and calculating a distance value, and assigning a cumulative distance value to the last core point in the matched sequence of core points, whereby a smallest cumulative distance value is assigned to the last core point and corresponds to a sequence of matched templates which represent a possible recognition result of the handwritten pattern.

Abstract: A method for recognition of a handwritten pattern comprises the steps of forming (4) a representation of the handwritten pattern, forming (6) at least two subconfigurations by dividing the representation of the handwritten pattern, and processing the subconfigurations. The step of processing comprises the steps of comparing (8) each subconfiguration with reference configurations, selecting (10) at least one subconfiguration candidate for each subconfiguration among the reference configurations based on said step of comparing, and determining (12) at least one candidate pattern consisting of one selected subconfiguration candidate for each subconfiguration. The method further comprises the steps of comparing (14) the representation of the handwritten pattern to the candidate pattern, and computing (16) a cost function in order to find a closest matching candidate pattern.

Abstract: A method for recognition of a handwritten character comprises the steps of determining a plurality of position features defining the handwritten character, and comparing the handwritten character to reference characters stored in a database in order to find the closest matching reference character. The step of comparing comprises the steps of computing a difference between one of the plurality of position features of the handwritten character and a corresponding position feature of one of the reference characters, determining, by lookup in a predefined table, a distance measure based on the computed difference and determining a distance measure for each of the plurality of position features of the handwritten character, and computing a cost function based on the determined distance measures. A device and a computer program for implementing the method are also described.

Abstract: A method for recognition of a handwritten pattern comprises the steps of forming (4) a representation of the handwritten pattern, forming (6) at least two sub-configurations by dividing the representation of the handwritten pattern, and processing the subconfigurations. The step of processing comprises the steps of comparing (8) each subconfiguration with reference configurations, selecting (10) at least one subconfiguration candidate for each subconfiguration among the reference configurations based on said step of comparing, and determining (12) at least one candidate pattern consisting of one selected subconfiguration candidate for each subconfiguration. The method further comprises the steps of comparing (14) the representation of the handwritten pattern to the candidate pattern, and computing (16) a cost function in order to find a closest matching candidate pattern.

Abstract: Character recognition includes detecting a union of characters, preprocessing the union of characters, comparing the preprocessed union of characters with one or more template symbols, and applying a decision rule to either reject a template symbol or decide that the template symbol is included in the union of characters. Such preprocessing involves representing the union of characters as one or more curves, and parameterizing the curve(s); and, regarding various classes of transformation, forming one or more shapes for the curve(s). The comparing operation involves forming one or more geometric proximity measures, and determining for every shape the values of those measures between the shape and correspondingly determined shapes for the template symbols. Applying a decision rule involves selecting one or more template symbols in consideration of the values.

Abstract: Character recognition includes detecting a union of characters, preprocessing the union of characters, comparing the preprocessed union of characters with one or more template symbols, and applying a decision rule to either reject a template symbol or decide that the template symbol is included in the union of characters. Such preprocessing involves representing the union of characters as one or more curves, and parameterizing the curve(s); and, regarding various classes of transformation, forming one or more shapes for the curve(s). The comparing operation involves forming one or more geometric proximity measures, and determining for every shape the values of those measures between the shape and correspondingly determined shapes for the template symbols. Applying a decision rule involves selecting one or more template symbols in consideration of the values.

Abstract: A method of character recognition includes detecting a union of characters, preprocessing the union of characters, comparing the preprocessed union of characters with one or more template symbols, and applying a decision rule in order to either reject a template symbol or decide that the template symbol is included in the union of characters. Preprocessing the union of characters involves representing the union of characters as one or more curves, and parameterising said curve or curves, and regarding various classes of transformations forming one or more shapes for the curve or curves. The comparing operation involves forming one or more geometric proximity measures, and determining for every shape the values of said geometric proximity measures between said shape and correspondingly determined shapes for the template symbols. Finally, applying a decision rule involves selecting one or more template symbols in consideration of the values.